OpenRaiser/Intern-Atlas huggingface dataset:
Advanced Target Detection via Molecular Communication
Venue: arXiv cs.ET 2018
Year: 2018

In this paper, we consider target detection in suspicious tissue via diffusive molecular communications (MCs). If a target is present, it continuously and with a constant rate secretes molecules of a specific type, so-called biomarkers, into the medium, which are symptomatic for the presence of the target. Detection of these biomarkers is challenging since due to the diffusion and degradation, the biomarkers are only detectable in the vicinity of the target. In addition, the exact location of the target within the tissue is not known. In this paper, we propose to distribute several reactive nanosensors (NSs) across the tissue such that at least some of them are expected to come in contact with biomarkers, which cause them to become activated. Upon activation, an NS releases a certain number of molecules of a secondary type into the medium to alert a fusion center (FC), where the final decision regarding the presence of the target is made. In particular, we consider a composite hypothesis testing framework where it is assumed that the location of the target and the biomarker secretion rate are unknown, whereas the locations of the NSs are known. We derive the uniformly most powerful (UMP) test for the detection at the NSs. For the final decision at the FC, we show that the UMP test does not exist. Hence, we derive a genie-aided detector as an upper bound on performance. We then propose two sub-optimal detectors and evaluate their performance via simulations
Approximating $(k,\ell)$-center clustering for curves
Venue: arXiv cs.CG 2018
Year: 2018

The Euclidean $k$-center problem is a classical problem that has been extensively studied in computer science. Given a set $\mathcal{G}$ of $n$ points in Euclidean space, the problem is to determine a set $\mathcal{C}$ of $k$ centers (not necessarily part of $\mathcal{G}$) such that the maximum distance between a point in $\mathcal{G}$ and its nearest neighbor in $\mathcal{C}$ is minimized. In this paper we study the corresponding $(k,\ell)$-center problem for polygonal curves under the Fréchet distance, that is, given a set $\mathcal{G}$ of $n$ polygonal curves in $\mathbb{R}^d$, each of complexity $m$, determine a set $\mathcal{C}$ of $k$ polygonal curves in $\mathbb{R}^d$, each of complexity $\ell$, such that the maximum Fréchet distance of a curve in $\mathcal{G}$ to its closest curve in $\mathcal{C}$ is minimized. In this paper, we substantially extend and improve the known approximation bounds for curves in dimension $2$ and higher. We show that, if $\ell$ is part of the input, then there is no polynomial-time approximation scheme unless $\mathsf{P}=\mathsf{NP}$. Our constructions yield different bounds for one and two-dimensional curves and the discrete and continuous Fréchet distance. In the case of the discrete Fréchet distance on two-dimensional curves, we show hardness of approximation within a factor close to $2.598$. This result also holds when $k=1$, and the $\mathsf{NP}$-hardness extends to the case that $\ell=\infty$, i.e., for the problem of computing the minimum-enclosing ball under the Fréchet distance. Finally, we observe that a careful adaptation of Gonzalez' algorithm in combination with a curve simplification yields a $3$-approximation in any dimension, provided that an optimal simplification can be computed exactly. We conclude that our approximation bounds are close to being tight.
Distributed State Estimation Using Intermittently Connected Robot Networks
Venue: arXiv cs.RO 2018
Year: 2018

This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

There are many machine translation (MT) papers that propose novel approaches and show improvements over their self-defined baselines. The experimental setting in each paper often differs from one another. As such, it is hard to determine if a proposed approach is really useful and advances the state of the art. Chinese-to-English translation is a common translation direction in MT papers, although there is not one widely accepted experimental setting in Chinese-to-English MT. Our goal in this paper is to propose a benchmark in evaluation setup for Chinese-to-English machine translation, such that the effectiveness of a new proposed MT approach can be directly compared to previous approaches. Towards this end, we also built a highly competitive state-of-the-art MT system trained on a large-scale training set. Our system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. We argue that a standardized benchmark on data and performance is important for meaningful comparison.
Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (Extended Version)
Venue: arXiv cs.LG 2018
Year: 2018

We study the Combinatorial Pure Exploration problem with Continuous and Separable reward functions (CPE-CS) in the stochastic multi-armed bandit setting. In a CPE-CS instance, we are given several stochastic arms with unknown distributions, as well as a collection of possible decisions. Each decision has a reward according to the distributions of arms. The goal is to identify the decision with the maximum reward, using as few arm samples as possible. The problem generalizes the combinatorial pure exploration problem with linear rewards, which has attracted significant attention in recent years. In this paper, we propose an adaptive learning algorithm for the CPE-CS problem, and analyze its sample complexity. In particular, we introduce a new hardness measure called the consistent optimality hardness, and give both the upper and lower bounds of sample complexity. Moreover, we give examples to demonstrate that our solution has the capacity to deal with non-linear reward functions.
5G Island for Network Resilience and Autonomous Failsafe Operations
Venue: arXiv cs.NI 2018
Year: 2018

The resilience of 5G networks can be strongly challenged by central cloud virtual network function (VNF) outages, which can be cause by server and backhaul connection errors. This paper proposes a context-aware approach to migrate VNF from central cloud to local edge cloud, in order to improve the network resilience with minimized VNF migration cost.
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging
Venue: arXiv cs.CV 2018
Year: 2018

Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifier on a set of feature vectors extracted from normal and pathological areas in patients' scans. However, many pathologies (such as epilepsy) are characterized by lesions that may be located anywhere in the brain, have various shapes, sizes and texture. An adequate representation of such a heterogeneity requires a significant amount of annotated data which is a major issue in the medical domain. Therefore, we built on a previously proposed approach that considers epilepsy lesion detection task as a voxel-level outlier detection problem. It consists in building a oc-SVM classifier for each voxel in the brain volume using a small number of clinically-guided features El Azami et al., 2016. Our goal in this study is to make a step forward by replacing the handcrafted features with automatically learnt representations using neural networks. We propose a novel version of siamese networks trained on patches extracted from healthy patients' scans only. This network, composed of stacked autoencoders as subnetworks, is regularized by the reconstruction error of the patches. It is designed to learn representations that bring patches centered at the same voxel localization 'closer' with respect to the chosen metric (i.e. cosine). Finally, the middle layer representations of the subnetworks are fed to oc-SVM classifiers at voxel-level. The method is validated on 3 patients' MRI scans with confirmed epilepsy lesions and shows a promising performance.
Two theorems about the P versus NP problem
Venue: arXiv cs.CC 2018
Year: 2018

Two theorems about the P versus NP problem be proved in this article (1) There exists a language $L$, that the statement $L \in \textbf{P}$ is independent of ZFC. (2) There exists a language $L \in \textbf{NP}$, for any polynomial time deterministic Turing machine $M$, we cannot prove $L$ is decidable on $M$.
To Centralize or Not to Centralize: A Tale of Swarm Coordination
Venue: arXiv cs.DC 2018
Year: 2018

Large swarms of autonomous devices are increasing in size and importance. When it comes to controlling the devices of large-scale swarms there are two main lines of thought. Centralized control, where all decisions - and often compute - happen in a centralized back-end cloud system, and distributed control, where edge devices are responsible for selecting and executing tasks with minimal or zero help from a centralized entity. In this work we aim to quantify the trade-offs between the two approaches with respect to task assignment quality, latency, and reliability. We do so first on a local swarm of 12 programmable drones with a 10-server cluster as the backend cloud, and then using a validated simulator to study the tail at scale effects of swarm coordination control. We conclude that although centralized control almost always outperforms distributed in the quality of its decisions, it faces significant scalability limitations, and we provide a list of system challenges that need to be addressed for centralized control to scale.
Unsupervised learning for concept detection in medical images: a comparative analysis
Venue: arXiv cs.CV 2018
Year: 2018

As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in the biomedical literature, which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature space evaluated using images from the ImageCLEF 2017 concept detection task. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches, in contrast with previously popular computer vision methods. Although generative adversarial networks can provide good results, they are harder to succeed in highly varied data sets. The possibility of semi-supervised learning, as well as their use in medical information retrieval problems, are the next steps to be strongly considered.
Object and Text-guided Semantics for CNN-based Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods have harvested the power of large text corpora to relate the objects and the activities to be transferred into learning a unified deep convolutional neural network. We present a novel activity recognition CNN which co-learns the object recognition task in an end-to-end multitask learning scheme to improve upon the baseline activity recognition performance. We further improve upon the multitask learning approach by exploiting a text-guided semantic space to select the most relevant objects with respect to the target activities. To the best of our knowledge, we are the first to investigate this approach.
Time-on-Task Estimation with Log-Normal Mixture Model
Venue: arXiv cs.HC 2018
Year: 2018

We describe a method of estimating a user's time-on-task in an online learning environment. The method is agnostic of the details of the user's mental activity and does not rely on any data except timestamps of user's interactions, accounting for individual user differences. The method is implemented in R (the code is open-source) and has been tested in the data from a large sample of HarvardX MOOCs.
Towards a Spectrum of Graph Convolutional Networks
Venue: arXiv cs.LG 2018
Year: 2018

We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where the aggregation operations are (a) determined by structural properties of the local neighborhood graphs and (b) not restricted to weighted averages. We show that the proposed approach is strictly more expressive while requiring only a modest increase in the number of parameters and computations. We also show that the proposed generalization is identical to standard convolutional layers when applied to regular grid graphs.
Polynomial data compression for large-scale physics experiments
Venue: arXiv cs.NA 2018
Year: 2018

The new generation research experiments will introduce huge data surge to a continuously increasing data production by current experiments. This data surge necessitates efficient compression techniques. These compression techniques must guarantee an optimum tradeoff between compression rate and the corresponding compression /decompression speed ratio without affecting the data integrity. This work presents a lossless compression algorithm to compress physics data generated by Astronomy, Astrophysics and Particle Physics experiments. The developed algorithms have been tuned and tested on a real use case~: the next generation ground-based high-energy gamma ray observatory, Cherenkov Telescope Array (CTA), requiring important compression performance. Stand-alone, the proposed compression method is very fast and reasonably efficient. Alternatively, applied as pre-compression algorithm, it can accelerate common methods like LZMA, keeping close performance.
Verifying Handcoded Probabilistic Inference Procedures
Venue: arXiv cs.PL 2018
Year: 2018

Researchers have recently proposed several systems that ease the process of performing Bayesian probabilistic inference. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this paper, we present Shuffle, a programming language for manually developing inference procedures that 1) enforces the basic rules of probability theory, 2) enforces the statistical dependencies of the algorithm's corresponding probabilistic model, and 3) generates an optimized implementation. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver correct and performant implementations of these algorithms.
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings
Venue: arXiv cs.LG 2018
Year: 2018

Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspects, like efficacy, visualization, and interpretability. In this paper, we propose, t-PINE, a method that addresses these limitations. Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph, the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view, in order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful, highly predictable, and gracefully interpretable representations. Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process. In addition, it helps us to exclude unrelated dimensions. Extensive experiments show that t-PINE drastically outperforms baseline methods by up to 158.6% with respect to Micro-F1, in several multi-label classification problems, while it has high visualization and interpretability utility.
Superconducting Optoelectronic Neurons III: Synaptic Plasticity
Venue: arXiv cs.NE 2018
Year: 2018

As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron. A standard flux memory cell is used to achieve a binary synapse, and loops capable of storing many flux quanta are used to enact multi-stable synapses. Circuits are designed to implement supervised learning wherein current pulses add or remove flux from the loop to strengthen or weaken the synaptic weight. Designs are presented for circuits with hundreds of intermediate synaptic weights between minimum and maximum strengths. Circuits for implementing unsupervised learning are modeled using two photons to strengthen and two photons to weaken the synaptic weight via Hebbian and anti-Hebbian learning rules, and techniques are proposed to control the learning rate. Implementation of short-term plasticity, homeostatic plasticity, and metaplasticity in loop neurons is discussed.
Superconducting Optoelectronic Neurons IV: Transmitter Circuits
Venue: arXiv cs.NE 2018
Year: 2018

A superconducting optoelectronic neuron will produce a small current pulse upon reaching threshold. We present an amplifier chain that converts this small current pulse to a voltage pulse sufficient to produce light from a semiconductor diode. This light is the signal used to communicate between neurons in the network. The amplifier chain comprises a thresholding Josephson junction, a relaxation oscillator Josephson junction, a superconducting thin-film current-gated current amplifier, and a superconducting thin-film current-gated voltage amplifier. We analyze the performance of the elements in the amplifier chain in the time domain to calculate the energy consumption per photon created for several values of light-emitting diode capacitance and efficiency. The speed of the amplification sequence allows neuronal firing up to at least 20\,MHz with power density low enough to be cooled easily with standard $^4$He cryogenic systems operating at 4.2\,K.
Various Approaches to Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as "the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.
Compressed Coded Distributed Computing
Venue: arXiv cs.IT 2018
Year: 2018

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme.
An SDP Primal-Dual Approximation Algorithm for Directed Hypergraph Expansion and Sparsest Cut with Product Demands
Venue: arXiv cs.DM 2018
Year: 2018

We give approximation algorithms for the edge expansion and sparsest cut with product demands problems on directed hypergraphs, which subsume previous graph models such as undirected hypergraphs and directed normal graphs. Using an SDP formulation adapted to directed hypergraphs, we apply the SDP primal-dual framework by Arora and Kale (JACM 2016) to design polynomial-time algorithms whose approximation ratios match those of algorithms previously designed for more restricted graph models. Moreover, we have deconstructed their framework and simplified the notation to give a much cleaner presentation of the algorithms.